2025/07/21 by Kunwoo Park, Park, Kunwoo, Ikbeom Lee +9
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Neural Networks and Reservoir Computing #Optics (physics.optics) #Quantum many-body systems
paper · pdf · doi:10.48550/arxiv.2507.15329
openalex publication_date 2025/07/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Hypergraphs offer a generalized framework for understanding complex systems, covering group interactions of different orders beyond traditional pairwise interactions. This modelling allows for the simplified description of simultaneous interactions among multiple elements in coupled oscillators, graph neural networks, and entangled qubits. Here, we employ this generalized framework to describe wave-matter interactions for material design acceleration. By devising the set operations for multiparticle systems, we develop the hypergraph model, which compactly describes wave interferences among multiparticles in scattering events by hyperedges of different orders. This compactness enables an evolutionary algorithm with O(N1/2) time complexity and approximated accuracy for designing stealthy hyperuniform materials, which is superior to traditional methods of O(N) scaling. By hybridizing our hypergraph evolutions to the conventional collective-coordinate method, we preserve the original accuracy, while achieving substantial speed-up in approaching near the optimum. Our result paves the way toward scalable material design and compact interpretations of large-scale multiparticle systems.